Download or read book Distributed Machine Learning with Communication Constraints written by Yuchen Zhang and published by . This book was released on 2016 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed machine learning bridges the traditional fields of distributed systems and machine learning, nurturing a rich family of research problems. Classical machine learning algorithms process the data by a single-thread procedure, but as the scale of the dataset and the complexity of the models grow rapidly, it becomes prohibitively slow to process on a single machine. The usage of distributed computing involves several fundamental trade-offs. On one hand, the computation time is reduced by allocating the data to multiple computing nodes. But since the algorithm is parallelized, there are compromises in terms of accuracy and communication cost. Such trade-offs puts our interests in the intersection of multiple areas, including statistical theory, communication complexity theory, information theory and optimization theory. In this thesis, we explore theoretical foundations of distributed machine learning under communication constraints. We study the trade-offs between communication and computation, as well as the trade-off between communication and learning accuracy. In particular settings, we are able to design algorithms that don't compromise on either side. We also establish fundamental limits that apply to all distributed algorithms. In more detail, this thesis makes the following contributions: * We propose communication-efficient algorithms for statistical optimization. These algorithms achieve the best possible statistical accuracy and suffer the least possible computation overhead. * We extend the same algorithmic idea to non-parametric regression, proposing an algorithm which also guarantees the optimal statistical rate and superlinearly reduces the computation time. * In the general setting of regularized empirical risk minimization, we propose a distributed optimization algorithm whose communication cost is independent of the data size, and is only weakly dependent on the number of machines. * We establish lower bounds on the communication complexity of statistical estimation and linear algebraic operations. These lower bounds characterize the fundamental limits of any distributed algorithm. * We design and implement a general framework for parallelizing sequential algorithms. The framework consists of a programming interface and an execution engine. The programming interface allows machine learning experts to implement the algorithm without concerning any detail about the distributed system. The execution engine automatically parallelizes the algorithm in a communication-efficient manner.

Download or read book Distributed Machine Learning and Computing written by M. Hadi Amini and published by Springer Nature. This book was released on with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Communication Efficient Federated Learning for Wireless Networks written by Mingzhe Chen and published by Springer Nature. This book was released on with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Federated Learning for Wireless Networks written by Choong Seon Hong and published by Springer Nature. This book was released on 2022-01-01 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.

Download or read book Scaling Up Machine Learning written by Ron Bekkerman and published by Cambridge University Press. This book was released on 2012 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

Download or read book Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems written by K. Suganthi and published by CRC Press. This book was released on 2021-09-14 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.

Download or read book Distributed Learning Systems with First order Methods written by Ji Liu and published by Now Publishers. This book was released on 2020-06-17 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this development is that recent progress has been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This monograph provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities but to fully realize the potential, it is essential they understand the whole picture. This monograph provides the bridge between the two communities. The simplified introduction to the essential aspects of each community enables researchers to gain insights into the factors influencing both. The monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research.

Download or read book Toward Robust and Communication Efficient Distributed Machine Learning written by Hongyi Wang and published by . This book was released on 2021 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed machine learning (ML) is emerging as its own field at the heart of MLSys due to the exploding scale of modern deep learning models and the enormous amounts of data. However, distributed ML suffers from low communication efficiency and is vulnerable to adversarial attacks. This dissertation focuses on improving the communication efficiency and robustness of distributed ML for two popular use cases, i.e., centralized distributed ML and federated learning (FL). The first part of this dissertation focuses on communication efficiency. For centralized distributed ML, we start by presenting Atomo, a general framework to compress the gradients via atomic sparsification. Improving Atomo, we present Pufferfish, which bypasses the need for gradient compression via integrating it into model training. Pufferfish trains the factorized low-rank model starting from its full-rank counterpart, which achieves both high communication and computation efficiency without the need of using any gradient compression. For FL, we propose FedMA, which uses matched averaging in a layer-wise manner instead of one-shot coordinate-wise averaging for model aggregation. FedMA effectively reduces the number of FL rounds needed for the global model to converge. The second part of this dissertation focuses on robustness. In the centralized setting, we present Draco, which leverages algorithmic redundancy to achieve Byzantine resilience with black-box convergence guarantees. To improve Draco, we present Detox, which combines robust aggregation with algorithmic redundancy. Detox can be used in tandem with any robust aggregation methods and enhances their Byzantine resilience and scalability. For FL, we demonstrate its vulnerability to training-time backdoors. We establish that robustness to backdoors implies model robustness to adversarial examples, a major open problem in itself. Furthermore, detecting the presence of a backdoor in an FL model is unlikely. We couple our results with edge-case backdoors, which forces a model to misclassify on seemingly easy inputs that are however unlikely to be part of the training or test data. We demonstrate that edge-case backdoors can lead to unsavory failures and may have serious repercussions on fairness and bypass all existing defense mechanisms.

Download or read book Distributed Artificial Intelligence written by Michael N. Huhns and published by Elsevier. This book was released on 2012-12-02 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed Artificial Intelligence presents a collection of papers describing the state of research in distributed artificial intelligence (DAI). DAI is concerned with the cooperative solution of problems by a decentralized group of agents. The agents may range from simple processing elements to complex entities exhibiting rational behavior. The book is organized into three parts. Part I addresses ways to develop control abstractions that efficiently guide problem-solving; communication abstractions that yield cooperation; and description abstractions that result in effective organizational structure. Part II describes architectures for developing and testing DAI systems. Part III discusses applications of DAI in manufacturing, office automation, and man-machine interactions. This book is intended for researchers, system developers, and students in artificial intelligence and related disciplines. It can also be used as a reference for students and researchers in other disciplines, such as psychology, philosophy, robotics, and distributed computing, who wish to understand the issues of DAI.

Download or read book Distributed Optimization and Learning written by Zhongguo Li and published by Elsevier. This book was released on 2024-08-06 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes. Provides a series of the latest results, including but not limited to, distributed cooperative and competitive optimization, machine learning, and optimal resource allocation Presents the most recent advances in theory and applications of distributed optimization and machine learning, including insightful connections to traditional control techniques Offers numerical and simulation results in each chapter in order to reflect engineering practice and demonstrate the main focus of developed analysis and synthesis approaches

Download or read book Machine Learning under Resource Constraints Applications written by Katharina Morik and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-12-31 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.

Download or read book Scalable and Distributed Machine Learning and Deep Learning Patterns written by Thomas, J. Joshua and published by IGI Global. This book was released on 2023-08-25 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scalable and Distributed Machine Learning and Deep Learning Patterns is a practical guide that provides insights into how distributed machine learning can speed up the training and serving of machine learning models, reduce time and costs, and address bottlenecks in the system during concurrent model training and inference. The book covers various topics related to distributed machine learning such as data parallelism, model parallelism, and hybrid parallelism. Readers will learn about cutting-edge parallel techniques for serving and training models such as parameter server and all-reduce, pipeline input, intra-layer model parallelism, and a hybrid of data and model parallelism. The book is suitable for machine learning professionals, researchers, and students who want to learn about distributed machine learning techniques and apply them to their work. This book is an essential resource for advancing knowledge and skills in artificial intelligence, deep learning, and high-performance computing. The book is suitable for computer, electronics, and electrical engineering courses focusing on artificial intelligence, parallel computing, high-performance computing, machine learning, and its applications. Whether you're a professional, researcher, or student working on machine and deep learning applications, this book provides a comprehensive guide for creating distributed machine learning, including multi-node machine learning systems, using Python development experience. By the end of the book, readers will have the knowledge and abilities necessary to construct and implement a distributed data processing pipeline for machine learning model inference and training, all while saving time and costs.

Download or read book Federated and Transfer Learning written by Roozbeh Razavi-Far and published by Springer Nature. This book was released on 2022-09-30 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.

Download or read book Near optimality of Distributed Network Management with a Machine Learning Approach written by Sung-eok Jeon and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: An analytical framework is developed for distributed management of large networks where each node makes locally its decisions. Two issues remain open. One is whether a distributed algorithm would result in a near-optimal management. The other is the complexity, i.e., whether a distributed algorithm would scale gracefully with a network size. We study these issues through modeling, approximation, and randomized distributed algorithms. For near-optimality issue, we first derive a global probabilistic model of network management variables which characterizes the complex spatial dependence of the variables. The spatial dependence results from externally imposed management constraints and internal properties of communication environments. We then apply probabilistic graphical models in machine learning to show when and whether the global model can be approximated by a local model. This study results in a sufficient condition for distributed management to be nearly optimal. We then show how to obtain a near-optimal configuration through decentralized adaptation of local configurations. We next derive a near-optimal distributed inference algorithm based on the derived local model. We characterize the trade-off between near-optimality and complexity of distributed and statistical management. We validate our formulation and theory through simulations.

Download or read book Distributed Optimization in Networked Systems written by Qingguo Lü and published by Springer Nature. This book was released on 2023-02-08 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.

Download or read book Machine Learning and Wireless Communications written by Yonina C. Eldar and published by Cambridge University Press. This book was released on 2022-08-04 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.

Download or read book Optimization Algorithms for Distributed Machine Learning written by Gauri Joshi and published by Springer Nature. This book was released on 2022-11-25 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.